Modeling and Fabrication of a Robot for Sowing in a Seedling Tray (Case Study: Sugar Beet)

Document Type : Research Article

Author

• S. Abdanan Mehdizadeh

Mechanics of Biosystems Engineering Department, Faculty of Agricultural Engineering and Rural Development, Agricultural Sciences and Natural Resources University of Khuzestan, Ahvaz, Iran

https://doi.org/10.22067/jam.2022.74055.1080

Abstract

Introduction
Adopting new technologies for crop growth has the characteristics of improving disaster resistance and stress tolerance, ensuring stable yields, and improving product quality. Currently, the cultivation of seed trays relies on huge labor power, and further mechanization is needed to increase production. However, there are some problems in this operation, such as the difficulty of improving the speed of a single machine, seedling deficiency detection, automatic planting, and controlling the quality, which need to be solved urgently. To solve these problems, there are already some meaningful attempts. Si et al. (2012) applied a photoelectric sensor to a vegetable transplanter, which can measure the distance between seedlings and the movement speed of seedlings in a seedling guide tube, to prevent omission transplantation. Yang et al. (2018) designed a seedling separation device with reciprocating movement of the seedling cup for rice transplanting. Tests show that the structure of the mechanical parts of the seedling separation device meets the requirements of seed movement. The optimization of the control system can improve the positioning accuracy according to requirements and achieve the purpose of automatic seedling division. Chen et al. (2020) designed and tested of soft-pot-tray automatic embedding system for a light-economical pot seedling nursery machine. The experimental results showed that the embedded-hard-tray automatic lowering mechanism was reliable and stable as the tray placement success rate was greater than 99%. The successful tray embedding rate was 100% and the seed exposure rate was less than 1% with a linear velocity of the conveyor belt of 0.92 m s^-1. The experiment findings agreed well with the analytical results.
Despite the sharp decline in Iran's water resources and growing population, the need to produce food and agricultural products is greater than ever. In the past, most seeds were planted directly into the soil, and many water resources, especially groundwater, were used for direct seed sowing and plant germination. One way to reduce the consumption of water, fertilizers, and pesticides is to plant seedlings instead of direct seed sowing. Therefore, the purpose of this study was dynamic modeling and fabrication of seed planting systems in seedling trays.
Material and Methods
In this experiment, Flores sugar beet seeds (Maribo company, Denmark) were used. The seedling trays had dimensions of 29.5*60 cm with openings and holes of 5.5 and 4 cm, respectively. To plant seeds in seedling trays, first, a planter arm was modeled and its position was obtained at any time. Then, based on dynamic modeling, the arm was constructed and a capacitive proximity sensor (CR30-15AC, China) and IR infrared proximity sensor (E18-D80NK, China) were used to find the location of seedling trays on the input conveyor and position of discharging arm, respectively. To achieve a stable and effective control system, a micro-controller-based circuit was developed to signal the planting system. The seed planting operation was performed in the seedling tray according to the coordinates which were provided through the image processing method. The planting system was evaluated at two levels of forward speed (5 and 10 cm s^-1). Moreover, a smartphone program was implemented to monitor the operation of the planting system.
Results and Discussion
The planting system was assessed for sugar beet seeds using two levels of forward speed (5 and 10
cm s^-1). The nominal capacity of this planter ranged from 3579 to 4613 cells per hour, with a miss and multiple implantation indices of 0.03% and 8.17%, respectively, in 3000 cells. Due to its planting accuracy, speed, and low energy consumption (25.56 watt-hours), this system has the potential to replace manual seeding in seedling trays.
Conclusion
In the present study, a seed-sowing system for planting seedling trays was designed, constructed, and evaluated based on dynamic modeling. In the developed system, unlike previous research, planting location detection was conducted through image processing. Additionally, a smartphone program was established to monitor the operation of the planting system without interfering with its performance. This study demonstrates that image processing can successfully detect planting locations and can effectively improve efficiency over time for major producers.

Keywords

• Image processing
• Modeling
• Planter system
• Seedling tray

20.1001.1.22286829.1402.13.3.1.2

Main Subjects

• Image Processing

©2022 The author(s). This article is licensed under Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source.

References

1. Abdolahzare, Z., & Abdanan Mehdizadeh, S. (2018a). Real time laboratory and field monitoring of the effect of the operational parameters on seed falling speed and trajectory of pneumatic planter. Computers and Electronics in Agriculture, 145, 187-198. https://doi.org/10.1016/j.compag.2018.01.001
2. Abdolahzare, Z., & Abdanan Mehdizadeh, S. (2018b). Nonlinear mathematical modeling of seed spacing uniformity of a pneumatic planter using genetic programming and image processing. Neural Computing and Applications, 29(2), 363-375. https://doi.org/10.1007/s00521-016-2450-1
3. Anonymous. (2007). Allegro MicroSystems, Fully Integrated, Hall Effect-Based Linear Current Sensor, ACS712 datasheet.
4. Biancardi, E., McGrath, M., William Panella, L., Lewellen, R.T. & Stevanato, P. (2010). Sugar Beet, Springer Publishers, NY. pp. 173-219. https://doi.org/10.1007/978-0-387-92765-7_6
5. Cao, J. (2019). Current situation and countermeasures of vegetable industry. Seed Science and Technology, 37(3), 18-21.
6. Chapra, S. C. (2012). Applied numerical methods with MATLAB for engineers and scientists, McGraw-Hill Publishers, NY., pp. 100-112.
7. Chen, L., Ma, X., Wang, C., Li, H., Li, Z., Chen, X., & Chen, T. (2020). Design and test of soft-pot-tray automatic embedding system for light-economical pot seedling nursery machine. International Journal of Agricultural and Biological Engineering, 13(1), 91-100. https://doi.org/10.25165/j.ijabe.20201301.4726
8. Food and Agriculture Organization (FAO). (2018). Retrieved from: http://www.fao.org/faostat/en/#data/QC
9. Gaikwad, B. B., & Sirohi, N.P.S. (2008). Design of a low-cost pneumatic seeder for nursery plug trays. Biosystems Engineering, 99(3), 322-329. https://doi.org/10.1016/j.biosystemseng.2007.10.017
10. Gezavati, J., Mohammad Zamani, D., Abbasgolipour, M., Mohammadi Alasti, B., & Ranji, A. (2014). Preliminary design, construction and evaluation of robot of tomato seed planting for the trays of greenhouse. Journal of Agricultural Machinery, 5(2), 242-250. (in Persian). https://doi.org/10.22067/jam.v5i2.28257
11. González Mendoza, J. M., Palacios Montufar, C., & Flores Campos, J. A. (2013). Analytical synthesis for four-bar mechanisms used in a pseudo-equatorial solar tracker. Ingeniería e Investigación, 33(3), 55-60. https://doi.org/10.15446/ing.investig.v33n3.41045.
12. Jin, X., Yuan, Y., Ji, J., Zhao, K., Li, M., & Chen, K. (2020). Design and implementation of anti-leakage planting system for transplanting machine based on fuzzy information. Computers and Electronics in Agriculture, 169, 105204. https://doi.org/10.1016/j.compag.2019.105204
13. Keyvanlo, A. L., & Armin, M. (2017). The effect of seedlings age and date of transfer on quantitative and qualitative characteristics of sugar beet. Iranian Journal of Field Crop Science, 48(1), 291-301. https://doi.org/10.22059/ijfcs.2017.131207.653935
14. Li-zhang, X. U., & Yao-ming, L. I. (2007). Design and simulation of the forest trees potter. Journal of Machinery Design & Manufacture, 3(2), 24-25.
15. Nadafzadeh, M., Abdanan Mehdizadeh, S., & Soltanikazemi, M. (2018). Development of computer vision system to predict peroxidase and polyphenol oxidase enzymes to evaluate the process of banana peel browning using genetic programming modeling. Scientia Horticulturae, 231(5), 201-209. https://doi.org/10.1016/j.scienta.2017.12.047
16. Nasri, R., Kashani, A., Sadeghian Motahar, Y., & Habibi, D. (2011). Quantitative and qualitative characteristics of fall sugar beet in direct cultivation and paper pot transplanting under saline soils of Ahvaz. Agronomy and Plant Breeding Journal, 7(7), 25-40. (In Persian).
17. Nematinia, E., & Mehdizadeh, S. A. (2018). Assessment of egg freshness by prediction of Haugh unit and albumen pH using an artificial neural network. Journal of Food Measurement and Characterization, 12(3), 1449-1459. https://doi.org/10.1007/s11694-018-9760-1
18. Rosli, M. F. M., Mahadi, M. R., Misri, M. A., & Wayayok, A. (2016). Initial design of an automated system for paddy seedling placement in a germination tray. Journal Technology, 78(1-2): 119-123. https://doi.org/10.11113/jt.v78.7283
19. Si, H. P., Sun, L., Jie, C., Wu, J. H., & Lin, K. Y. (2013). Summary of the developing status of greenhouse tray seeder and seed metering device. In Applied Mechanics and Materials, 387(8), 271-279. https://doi.org/10.4028/www.scientific.net/AMM.387.271
20. Tian, L., Xing, P. G., & Zhang, L. (2017). Design of automatic transplant device for hole disc seedlings based on PLC and photoelectric sensing control. Journal of Agriculture Machinery Research, 39(7), 125-129.
21. Waldron, K. J., Kinzel, G. L., & Agrawal, S. K. (2016). Kinematics, dynamics, and design of machinery. John Wiley & Sons Publishers. pp. 80-92. https://worldcat.org/en/title/930875622
22. Wang, J., Fu, Z., Zhang, B., Yang, F., Zhang, L., & Shi, B. (2018). Decomposition of influencing factors and its spatial-temporal characteristics of vegetable production: A case study of China. Information Processing in Agriculture, 5(4), 477-489. https://doi.org/10.1016/j.inpa.2018.06.004
23. Wang, X., & Feng, Q. C. (2013). Design and Simulation for Key Components of Robotic Flower-Seedling Transplanter.Advanced Materials Research, 680(11), 387-391. https://doi.org/10.4028/www.scientific.net/AMR.680.387
24. Yun, Zh., Xiong, Zh., Weiwei, Z., & Li, D. (2011). Modern design theory and method of rice transplanter.Transactions of the Chinese Society for Agricultural Machinery, 3(2), 013.
25. Yang, Q., Huang, G., Shi, X., He, M., Ahmad, I., Zhao, X., & Addy, M. (2020). Design of a control system for a mini-automatic transplanting machine of plug seedling. Computers and Electronics in Agriculture, 169, 105226. https://doi.org/10.1016/j.compag.2020.105226
26. Yang2018, Q., Xu, L., Shi, X., Ibrar, A., Mao, H., Hu, J., & Han, L. (2018). Design of seedlings separation device with reciprocating movement seedling cups and its controlling system of the full-automatic plug seedling transplanter. Computers and Electronics in Agriculture, 147, 131-145. https://doi.org/10.1016/j.compag.2018.02.004